pacman::p_load(sf, tmap, tidyverse, stplanr, DT)3: Processing and Visualising Flow Data
Spatial interaction represents the flow of people, material, or information between locations in geographical space. It encompasses everything from freight shipments, energy flows, and the global trade in rare antiquities, to flight schedules, rush hour woes, and pedestrian foot traffic.
Each spatial interaction is composed of a discrete origin/destination pair. Each pair can be represented as a cell in a matrix where rows are related to the locations (centroids) of origin, while columns are related to locations (centroids) of destination. Such a matrix is commonly known as an origin/destination matrix, or a spatial interaction matrix.
Getting Started
The code chunk below uses p_load() of pacman package to check if the required packages have been installed on the computer. If they are, the packages will be launched.
- sf package is used for importing, managing, and processing geospatial data.
- tmap package is used for thematic mapping.
- tidyverse package is used for importing, integrating, wrangling and visualising data.
- stplanr package is used for plotting desire lines on maps.
Preparing Flow Data
Import OD data
Firstly, import the Passenger Volume by Origin Destination Bus Stops data set downloaded from LTA DataMall by using read_csv() of readr package.
odbus <- read_csv("data/aspatial/origin_destination_bus_202310.csv")Display the odbus tibble data table by using the code chunk below.
glimpse(odbus)Rows: 5,694,297
Columns: 7
$ YEAR_MONTH <chr> "2023-10", "2023-10", "2023-10", "2023-10", "2023-…
$ DAY_TYPE <chr> "WEEKENDS/HOLIDAY", "WEEKDAY", "WEEKENDS/HOLIDAY",…
$ TIME_PER_HOUR <dbl> 16, 16, 14, 14, 17, 17, 17, 7, 14, 14, 10, 20, 20,…
$ PT_TYPE <chr> "BUS", "BUS", "BUS", "BUS", "BUS", "BUS", "BUS", "…
$ ORIGIN_PT_CODE <chr> "04168", "04168", "80119", "80119", "44069", "2028…
$ DESTINATION_PT_CODE <chr> "10051", "10051", "90079", "90079", "17229", "2014…
$ TOTAL_TRIPS <dbl> 3, 5, 3, 5, 4, 1, 24, 2, 1, 7, 3, 2, 5, 1, 1, 1, 1…
A quick check of odbus tibble data frame shows that the values in ORIGIN_PT_CODE and DESTINATON_PT_CODE are in numeric data type. Hence, the code chunk below is used to convert these data values into character data type.
odbus$ORIGIN_PT_CODE <- as.factor(odbus$ORIGIN_PT_CODE)
odbus$DESTINATION_PT_CODE <- as.factor(odbus$DESTINATION_PT_CODE) Extract commuting flow data
The code chunk below extracts the commuting flows on weekdays between 6 and 9 o’clock.
odbus6_9 <- odbus %>%
filter(DAY_TYPE == "WEEKDAY") %>%
filter(TIME_PER_HOUR >= 6 &
TIME_PER_HOUR <= 9) %>%
group_by(ORIGIN_PT_CODE,
DESTINATION_PT_CODE) %>%
summarise(TRIPS = sum(TOTAL_TRIPS))The table below shows the content of odbus6_9
datatable(odbus6_9)The output is saved in rds format for future use.
write_rds(odbus6_9, "data/rds/odbus6_9.rds")The code chunk below will be used to import the saved odbus6_9.rds into R environment.
odbus6_9 <- read_rds("data/rds/odbus6_9.rds")Working with Geospatial data
For this exercise, two geospatial data will be used:
- BusStop: This data provides the location of bus stop as at last quarter of 2022.
- MPSZ-2019: This data provides the sub-zone boundary of URA Master Plan 2019.
Both data sets are in ESRI shapefile format.
Import geospatial data
The two code chunks below are used to import both sets of geospatial data.
st_read()function of sf package is used to import the shapefile into R as sf data frame.st_transform()function of sf package is used to transform the projection to crs 3414.
BusStop <- st_read(dsn = "data/geospatial", layer = "BusStop") %>%
st_transform(crs = 3414)Reading layer `BusStop' from data source
`C:\magdalenecjw\ISSS624 Geospatial\Hands_on_Exercise\Ex3\data\geospatial'
using driver `ESRI Shapefile'
Simple feature collection with 5161 features and 3 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 3970.122 ymin: 26482.1 xmax: 48284.56 ymax: 52983.82
Projected CRS: SVY21
mpsz <- st_read(dsn = "data/geospatial", layer = "MPSZ-2019") %>%
st_transform(crs = 3414)Reading layer `MPSZ-2019' from data source
`C:\magdalenecjw\ISSS624 Geospatial\Hands_on_Exercise\Ex3\data\geospatial'
using driver `ESRI Shapefile'
Simple feature collection with 332 features and 6 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 103.6057 ymin: 1.158699 xmax: 104.0885 ymax: 1.470775
Geodetic CRS: WGS 84
The code chunk below will be used to write the mpsz sf tibble data frame into an rds file for future use.
mpsz <- write_rds(mpsz, "data/rds/mpsz.rds")Geospatial Data Wrangling
The code chunk below populates the planning subzone code (i.e. SUBZONE_C) of mpsz sf data frame into busstop sf data frame.
st_intersection()is used to perform point and polygon overlay and the output will be in point sf object.select()of dplyr package is then use to retain onlyBUS_STOP_NandSUBZONE_Cin thebusstop_mpszsf data frame. Five bus stops are excluded in the resultant data frame as they are outside of Singapore’s boundary.
busstop_mpsz <- st_intersection(BusStop, mpsz) %>%
select(BUS_STOP_N, SUBZONE_C) %>%
st_drop_geometry()
datatable(busstop_mpsz)Before moving to the next step, save the output into rds format.
write_rds(busstop_mpsz, "data/rds/busstop_mpsz.rds") Next, append the planning subzone code from busstop_mpsz data frame onto odbus6_9 data frame.
od_data <- left_join(odbus6_9 , busstop_mpsz,
by = c("ORIGIN_PT_CODE" = "BUS_STOP_N")) %>%
rename(ORIGIN_BS = ORIGIN_PT_CODE,
ORIGIN_SZ = SUBZONE_C,
DESTIN_BS = DESTINATION_PT_CODE)It is good practice to check for duplicating records before continuing further with the data wrangling.
duplicate <- od_data %>%
group_by_all() %>%
filter(n()>1) %>%
ungroup()
duplicate# A tibble: 1,186 × 4
ORIGIN_BS DESTIN_BS TRIPS ORIGIN_SZ
<chr> <fct> <dbl> <chr>
1 11009 01341 1 QTSZ01
2 11009 01341 1 QTSZ01
3 11009 01411 4 QTSZ01
4 11009 01411 4 QTSZ01
5 11009 01421 17 QTSZ01
6 11009 01421 17 QTSZ01
7 11009 01511 19 QTSZ01
8 11009 01511 19 QTSZ01
9 11009 01521 2 QTSZ01
10 11009 01521 2 QTSZ01
# … with 1,176 more rows
As there duplicated records found, the code chunk below will be used to retain the unique records.
od_data <- unique(od_data)Check that the duplicating records issue has now been resolved.
duplicate <- od_data %>%
group_by_all() %>%
filter(n()>1) %>%
ungroup()
duplicate# A tibble: 0 × 4
# … with 4 variables: ORIGIN_BS <chr>, DESTIN_BS <fct>, TRIPS <dbl>,
# ORIGIN_SZ <chr>
Next, update the od_data data frame with the planning subzone codes.
od_data <- left_join(od_data , busstop_mpsz,
by = c("DESTIN_BS" = "BUS_STOP_N")) Again, check for duplicates and retain unique records.
Show code
duplicate <- od_data %>%
group_by_all() %>%
filter(n()>1) %>%
ungroup()
od_data <- unique(od_data)od_data <- od_data %>%
rename(DESTIN_SZ = SUBZONE_C) %>%
drop_na() %>%
group_by(ORIGIN_SZ, DESTIN_SZ) %>%
summarise(MORNING_PEAK = sum(TRIPS))Save the output into an rds file format.
write_rds(od_data, "data/rds/od_data.rds")od_data <- read_rds("data/rds/od_data.rds")Visualising Spatial Interaction
Desire lines are rays connecting a site to associated location points. This section details the steps to prepare a desire line using the stplanr package.
Remove intra-zonal flows
The code chunk below will be used to remove intra-zonal flows which will not be plotted.
od_data1 <- od_data[od_data$ORIGIN_SZ!=od_data$DESTIN_SZ,]Create desire lines
In this code chunk below, od2line() of stplanr package is used to create the desire lines.
flowLine <- od2line(flow = od_data1,
zones = mpsz,
zone_code = "SUBZONE_C")Visualise desire lines
To visualise the resulting desire lines, the code chunk below is used. > Rendering may take longer due to the transparency argument i.e. alpha.
tm_shape(mpsz) +
tm_polygons() +
flowLine %>%
tm_shape() +
tm_lines(lwd = "MORNING_PEAK",
style = "quantile",
scale = c(0.1, 1, 3, 5, 7, 10),
n = 6,
alpha = 0.3)
In the chart above, the flow data is very messy and highly skewed. As such, it may be better to focus on selected flows, such as flows greater than or equal to 5000 as shown below.
tm_shape(mpsz) +
tm_polygons() +
flowLine %>%
filter(MORNING_PEAK >= 5000) %>%
tm_shape() +
tm_lines(lwd = "MORNING_PEAK",
style = "quantile",
scale = c(0.1, 1, 3, 5, 7, 10),
n = 6,
alpha = 0.3)